The target distribution \(\mathbf {p}\)  aims to improve cluster purity and to emphasize data points with high assignment confidence [1]}. Our preliminary design adapted this idea to the multi-view problem setting by using a separate target distribution for text and images sub-models separately. But we found that aligning both images and text to the same joint target distribution simplified the model, improved performance, and was more robust to noise. The JECL target distribution is as follows.
\(p_{ij} = \lambda \times \frac{{q^2_{ij}}{\sum _i q_{ij}}}{\sum _{j^{\prime }} {q^2_{ij^{\prime }}}{\sum _i q_{ij^{\prime }}}} + (1 - \lambda ) \times \frac{{r^2_{ij}}{\sum _i r_{ij}}}{ \sum _{j^{\prime }}{r^2_{ij^{\prime }}}{\sum _i r_{ij^{\prime }}}}\) 
